The Human Factor (in the Age of Machines)

We often say someone is “smart” when she can calculate numbers in her head really fast. During my childhood in Far East Asia, it was a fad to take abacus lessons. And some protégés could add a long series of 10-digit numbers in their heads without even touching the abacus.

Where are they now? Yeah, they may still be able to invoke some “Woos” and “Ahhs” from a TV audience, but they won’t be able to get a job at NASA to calculate trajectories of a spaceship just with those skills. Again, why bother with skills that even the smallest computer could do better? But plotting new tasks for the spaceship? Now that sounds like a human function, even in the Starship Enterprise — with unlimited computing power.

We also say someone is “smart” when he can connect dots among seemingly unrelated things or phenomena. Call it intuition, or pattern recognition in computing terms, but there are people who are better than others in identifying previously undetected correlations in various fields. Does one need scientific training to get better at that? Yes. Are machines catching up with us in that area, too? Definitely yes. But who would provide the “purpose” for such exploration? Who would come up with the initial hypothesis, justify the whole study, and ultimately answer the ever-important “so what” question?

Machines Are Smart. So What?

My dogs have Facebook accounts. (Don’t ask why.) And the other day, I noticed that Facebook put the younger one’s age in dog years on his birthday. It is quite interesting to see that now, the face recognition algorithms can differentiate different types of animals, as I’ve read a while back that it hasn’t been easy for a machine to differentiate pictures of dogs and cats. Apparently, they can do that now, and it is not that surprising.

One of the best features of machine learning is pattern recognition. And with enough training iterations with empirical data, why is it even surprising that they recognized my dog’s face? Give a machine some more time to play with it, it may be able to calculate the level of cuteness of a dog, eventually (based on collected human responses, of course). But who conceived the initial idea that putting our dog’s age in dog years would indeed be “funny” to humans? I can bet my farm that it was a human decision.

We also say someone is “smart” when he or she is creative, witty, humorous (without being a jackass) or intuitive. We call people “wise” when they see things beyond obvious patterns and consider even unintended consequences of actions. That's quite the opposite of one-dimensional folks. (These are the people who would instill purposes in our collective behavior and machine activities, alike.)

Machines Are Smart. Humans Are Smarter.

We now call machines “smart,” only because they came a long way since the invention of computers. But let’s face it. There wasn’t a single year when the storage capacity and computing speed did not improve significantly. We are just passing through the inevitable path of evolution.

When we say machines are smart, it means it remembers things really well, and calculate things really fast. Now it can recognize patterns, fill in the gaps and predict bits of the future. And they are moving into the stage of improving themselves without our constant intervention. That independent part may scare some people, and that’s only natural — considering our own violent history.

We call cars smart when they can navigate without a driver. But let me point out that such an intelligence level is equivalent to ants marching back to their home base. Heck, bumble bees do that in 3D space, but we don’t call them “smart.” If the machine can reason “why” it must head home at a certain time without any human instruction, I will then call it a true thinking machine.

Machine to Marketer: Take Me to Your Leader

Dialing back to present day, all of those analytical tools with fancy names out there? Supervised or unsupervised learnings alike, they are nothing without clear purposes. Do not think for a second that “your” problems will be defined by the machines and they will magically provide answers to you. At least not yet. And even if one day machines can do that, would you want to be defined by machines?

The human factor is not to be lost in the age of abundant data, ubiquitous connections and virtually unlimited computing power. Humanization of data and analytics practices is the key for success, as setting the business purpose will remain as a human function, and the results of analyses are nothing without adaptions by human decision-makers.

Blind implementation of automated learning by machines will never help any business. No matter how predictive machine learning has become, deciding what to predict is the hardest part of the process. Adaptation of the data-based decision-making process depends on the intuitiveness of solutions, not just complexity and speed factors.

Therefore, taking the best-of-the-both-worlds approach, even the simplest implementation of machine learning must be justified by proper solutioning framework from a business point of view, instead of pursuing the latest technologies for the sake of being cutting-edge.

Machines are becoming smarter by leaps and bounds. And maybe someday, they may even be sentient — with clear purposes of their existence. How do we human marketers keep up with such rapid evolution and stay relevant?

We must stay logical and be in a position to provide purposes of their activities, as even the most powerful computer in the world won’t understand illogical instructions. The capability to translate seemingly illogical human goals into terms even a machine can understand will be the future function of computing and analytics professionals. And ironically enough, we will need to cherish the human factors to coexist with machines successfully, and harness their full potential for our benefits.

Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is principal and chief product officer at BuyerGenomics. Previously, Yu was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, he was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at syu@buyergenomics.com.